Alternative methods for regulatory toxicology – a state-of-the-art review

This state-of-the art review is based on the final report of a project carried out by the European Commission’s Joint Research Centre (JRC) for the European Chemicals Agency (ECHA). The aim of the project was to review the state of the science of non-standard methods that are available for assessing the toxicological and ecotoxicological properties of chemicals. Non-standard methods refer to alternatives to animal experiments, such as in vitro tests and computational models, as well as animal methods that are not covered by current regulatory guidelines. This report therefore reviews the current scientific status of non-standard methods for a range of human health and ecotoxicological endpoints, and provides a commentary on the mechanistic basis and regulatory applicability of these methods. For completeness, and to provide context, currently accepted (standard) methods are also summarised. In particular, the following human health endpoints are covered: a) skin irritation and corrosion; b) serious eye damage and eye irritation; c) skin sensitisation; d) acute systemic toxicity; e) repeat dose toxicity; f) genotoxicity and mutagenicity; g) carcinogenicity; h) reproductive toxicity (including effects on development and fertility); i) endocrine disruption relevant to human health; and j) toxicokinetics. In relation to ecotoxicological endpoints, the report focuses on non-standard methods for acute and chronic fish toxicity. While specific reference is made to the information needs of REACH, the Biocidal Products Regulation and the Classification, Labelling and Packaging Regulation, this review is also expected to be informative in relation to the possible use of alternative and non-standard methods in other sectors, such as cosmetics and plant protection products.JRC.I.5-Systems Toxicolog

Evaluation of a novel method of predicting estrogen activity of a group of structurally diverse compounds

The number of environmental chemicals found to have some level of endocrine activity has led to concern about the possible effects these compounds could have on human health and the health of other species, populations, and possibly whole ecosystems. The United States Environmental Protection Agency has been charged with testing a large number of these compounds, called endocrine-disrupting chemicals or hormonally active agents for hormonal activity. Limited testing resources have led to a call for alternate methods of screening, possibly for use in prioritizing this list to assist in efficient allocation of resources for further testing. This study describes a computational method, the categorical structure activity relationship (cat-SAR) program, which has demonstrated high predictivity for the estrogen-like activity of a set of diverse chemical structures. The data set for this model was taken from a set of 122 compounds assayed for estrogenicity with the ESCREEN assay, an in vitro assay for estrogenicity. Two endpoints were modeled. The model for relative proliferative potency demonstrated an 82% correct prediction rate, while the relative proliferative effect achieved an 86% correct rate of prediction in model validation. Preliminary evaluation of fragments upon which the models were based suggested a sound mechanistic basis. The models also compared similarly to previous ESCREEN models developed using a different methodology. Based on the results described herein, the cat-SAR method would be a useful approach in screening compounds for estrogen activity as well as for investigating their mechanism of action

Systems and chemical biology approaches to study cell function and response to toxins

Toxicity is one of the main causes of failure during drug discovery, and of withdrawal once drugs reached the market. Prediction of potential toxicities in the early stage of drug development has thus become of great interest to reduce such costly failures. Since toxicity results from chemical perturbation of biological systems, we combined biological and chemical strategies to help understand and ultimately predict drug toxicities. First, we proposed a systematic strategy to predict and understand the mechanistic interpretation of drug toxicities based on chemical fragments. Fragments frequently found in chemicals with certain toxicities were defined as structural alerts for use in prediction. Some of the predictions were supported with mechanistic interpretation by integrating fragmentchemical, chemical-protein, protein-protein interactions and gene expression data. Next, we systematically deciphered the mechanisms of drug actions and toxicities by analyzing the associations of drugs’ chemical features, biological features and their gene expression profiles from the TG-GATEs database. We found that in vivo (rat liver) and in vitro (rat hepatocyte) gene expression patterns were poorly overlapped and gene expression responses in different species (rat and human) and different tissues (liver and kidney) varied widely. Eventually, for further understanding of individual differences in drug responses, we reviewed how genetic polymorphisms influence the individual's susceptibility to drug toxicity by deriving chemical-protein interactions and SNP variations from Mechismo database. Such a study is also essential for personalized medicine. Overall, this study showed that, integrating chemical and biological in addition to genetic data can help assess and predict drug toxicity at system and population levels

Autoencoded DNA methylation data to predict breast cancer recurrence: Machine learning models and gene-weight significance

Breast cancer is the most frequent cancer in women and the second most frequent overall after lung cancer. Although the 5-year survival rate of breast cancer is relatively high, recurrence is also common which often involves metastasis with its consequent threat for patients. DNA methylation-derived databases have become an interesting primary source for supervised knowledge extraction regarding breast cancer. Unfortunately, the study of DNA methylation involves the processing of hundreds of thousands of features for every patient. DNA methylation is featured by High Dimension Low Sample Size which has shown well-known issues regarding feature selection and generation. Autoencoders (AEs) appear as a specific technique for conducting nonlinear feature fusion. Our main objective in this work is to design a procedure to summarize DNA methylation by taking advantage of AEs. Our proposal is able to generate new features from the values of CpG sites of patients with and without recurrence. Then, a limited set of relevant genes to characterize breast cancer recurrence is proposed by the application of survival analysis and a pondered ranking of genes according to the distribution of their CpG sites. To test our proposal we have selected a dataset from The Cancer Genome Atlas data portal and an AE with a single-hidden layer. The literature and enrichment analysis (based on genomic context and functional annota tion) conducted regarding the genes obtained with our experiment confirmed that all of these genes were related to breast cancer recurrence.Ministerio de Economía y Competitividad TIN2014-55894-C2-RMinisterio de Economía y Competitividad TIN2017-88209-C2-2-

2016 Research Week

RESEARCH WEEK 2016 TABLE OF CONTENTS ADMINISTRATION RESEARCH COMMITTEES ACTIVITY SCHEDULE LETTERS OF ENDORSEMENT GENERAL SESSION FACULTY, STAFF, AND STUDENT POSTER PRESENTATIONS FACULTY AND STUDENT ORAL PRESENTATION

A Knowledge Based Approach of Toxicity Prediction for Drug Formulation. Modelling Drug Vehicle Relationships Using Soft Computing Techniques

This multidisciplinary thesis is concerned with the prediction of drug formulations for the reduction of drug toxicity. Both scientific and computational approaches are utilised to make original contributions to the field of predictive toxicology. The first part of this thesis provides a detailed scientific discussion on all aspects of drug formulation and toxicity. Discussions are focused around the principal mechanisms of drug toxicity and how drug toxicity is studied and reported in the literature. Furthermore, a review of the current technologies available for formulating drugs for toxicity reduction is provided. Examples of studies reported in the literature that have used these technologies to reduce drug toxicity are also reported. The thesis also provides an overview of the computational approaches currently employed in the field of in silico predictive toxicology. This overview focuses on the machine learning approaches used to build predictive QSAR classification models, with examples discovered from the literature provided. Two methodologies have been developed as part of the main work of this thesis. The first is focused on use of directed bipartite graphs and Venn diagrams for the visualisation and extraction of drug-vehicle relationships from large un-curated datasets which show changes in the patterns of toxicity. These relationships can be rapidly extracted and visualised using the methodology proposed in chapter 4. The second methodology proposed, involves mining large datasets for the extraction of drug-vehicle toxicity data. The methodology uses an area-under-the-curve principle to make pairwise comparisons of vehicles which are classified according to the toxicity protection they offer, from which predictive classification models based on random forests and decisions trees are built. The results of this methodology are reported in chapter 6

Molecular characterization of phenothiazines in experimental cancer therapy - New tricks of an old drug revealed

Cancer is characterized by uncontrolled malignant proliferation of cells that eventually interfere with tissue/organ functions. Traditionally, cancer is treated with chemo- and/or radiotherapy when surgery is not an option. Unfortunately, the efficacy of conventional anti-cancer chemotherapy is severely limited by therapy resistance. A conceptually appealing strategy to combat tumor resistance is to use chemosensitizers, compounds that selectively sensitize tumor cells to chemotherapy without affecting normal tissue. Phenothiazines belong to a class of “old” drugs that are used clinically to treat psychiatric disorders. In this thesis, we characterized the chemosensitizing potential of phenothiazines in combination with DNA damaging chemotherapeutic drugs. Our primary aims are to elucidate the molecular mechanisms by which phenothiazines impart sensitization and to delineate molecular determinants that predict responsiveness of tumors to phenothiazine-based intervention. In Paper I, we confirmed that the phenothiazine compound trifluoperazine (TFP) was a potent sensitizer of bleomycin in human non-small cell lung carcinoma (NSCLC) cells; the likely mechanism being inhibition of repair of DNA single strand breaks (SSB) as well as DNA double strand breaks (DSB). In Paper II, we found that TFP delayed the resolution of bleomycin- or cisplatin-induced γH2AX, a marker of unrepaired DNA DSB, prolonged the cell cycle arrest and increased oxidative stress in NSCLC cells. TFP co-treated cells eventually resumed cycling without fully repairing the DNA damage, which led to mitotic defects, secondary checkpoint arrest, exacerbated oxidative stress, organelle dysfunction, caspase activation and ultimately apoptosis. In Paper III, we uncovered a possible link between phenothiazines and chromatin remodeling by in silico gene expression analysis. We found that TFP and structurally related phenothiazines significantly enhanced the activity DNA-PK/ATM in tumor but not normal fibroblasts in response to DNA DSB-inducing agents, resulting in increased selective phosphorylation of a subset of ATM substrates with chromatin regulatory functions. Notably, this represents an adaptive response which could be targeted by DNA-PK/ATM inhibitors to further enhance TFP-mediated chemosensitization in NSCLC cells. Moreover, we found that wild-type p53 is a potential predictor of unresponsiveness to phenothiazine-based chemosensitization. We further demonstrated that TFP preferentially increased the cytotoxicity of direct-acing DNA damaging agents, but not indirect-acting DNA damaging or non-DNA damaging agents, in p53-deficient tumor cells (NSCLC, breast cancer). In Paper IV, we compared the gene expression profile of NSCLC residual clones that survived cisplatin treatment with counterparts that survived cisplatin/TFP co-treatment. We found that survival after cisplatin was associated with enrichment of pathways involved in DNA metabolism/repair, cell cycle and RNA post-translational modification. Pathway analysis showed that several DNA repair genes were concurrently up-regulated in residual clones that survived cisplatin treatment, but not in residual clones that survived cisplatin/TFP co-treatment did not shown such enrichment of DNA repair genes. In summary, our data showed for the first time that inhibition of DNA DSB repair by TFP is related to alterations in DNA-PK/ATM signaling, which led to increased apoptosis in the short term and gene expression changes as well as loss of clonogenicity in the long term. Further, our identification of molecular contexts that predict responsiveness to phenothiazines will aid in the design of future clinical trials

Computer aided drug design: Drug target directed in silico approaches

Ph.DDOCTOR OF PHILOSOPH

Technology, Science and Culture

From the success of the first and second volume of this series, we are enthusiastic to continue our discussions on research topics related to the fields of Food Science, Intelligent Systems, Molecular Biomedicine, Water Science, and Creation and Theories of Culture. Our aims are to discuss the newest topics, theories, and research methods in each of the mentioned fields, to promote debates among top researchers and graduate students and to generate collaborative works among them